P4_Trachea_10X_epithelial

P4 trachea sample collected on Oct18 2018. epithelial subset

library(Seurat)
library(dplyr)

basal, secretory, and ciliated:

P4_10X_mm10_1.2.0 <- SetAllIdent(object = P4_10X_mm10_1.2.0, id = "res.0.8")
P4_Oct18_epi<-SubsetData(object=P4_10X_mm10_1.2.0,ident.use=c(1:5,8,11))
table(P4_Oct18_epi@meta.data$res.0.8,P4_Oct18_epi@meta.data$seq_group)

    
     P4_Oct18_mut_green P4_Oct18_mut_red P4_Oct18_wt_green P4_Oct18_wt_red
  1                 225                0               149               2
  11                107                0                58               1
  2                  28                0               278               1
  3                 163                0               143               0
  4                 285                0                11               0
  5                 167                0               125               0
  8                 195                0                24               0

colnames(P4_Oct18_epi@meta.data)[colnames(P4_Oct18_epi@meta.data) == 'res.0.8'] <- 'orig.0.8'
P4_Oct18_epi <- ScaleData(object = P4_Oct18_epi)

Scaling data matrix

  |                                                                                                                                                  
  |                                                                                                                                            |   0%
  |                                                                                                                                                  
  |============================================================================================================================================| 100%

P4_Oct18_epi <- FindVariableGenes(object = P4_Oct18_epi, do.plot = TRUE, x.low.cutoff=0.1,x.high.cutoff = Inf, y.cutoff = 0.5)

Calculating gene means
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variance to mean ratios
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

run PCA on the set of genes

P4_Oct18_epi <- RunPCA(object = P4_Oct18_epi, do.print = FALSE)
PCAPlot(P4_Oct18_epi)

P4_Oct18_epi <- ProjectPCA(object = P4_Oct18_epi, do.print = F)

PCElbowPlot(object = P4_Oct18_epi)

PCHeatmap(object = P4_Oct18_epi, pc.use = 1:20, cells.use = 500, do.balanced = TRUE, label.columns = FALSE, num.genes = 25)

n.pcs.sub = 16

res.used <- 0.8

P4_Oct18_epi <- FindClusters(object = P4_Oct18_epi, reduction.type = "pca", dims.use = 1:n.pcs.sub, 
                     resolution = res.used, print.output = 0, force.recalc = T)

P4_Oct18_epi <- RunTSNE(object = P4_Oct18_epi, dims.use = 1:n.pcs.sub, perplexity=30)

TSNEPlot(object = P4_Oct18_epi, do.label = T,pt.size = 0.4,group.by="res.0.8")

P4_Oct18_epi@meta.data$cell_type<-mapvalues(P4_Oct18_epi@meta.data$res.0.8,from=c("0","1","2","3","4","5","6","7","8","9"),to=c("Secretory","Secretory","Ciliated","Secretory","CiliaSecretory","Secretory","Basal","Basal","Ciliated","Ciliated"))

P4_epi_cellType<-P4_Oct18_epi@meta.data$cell_type
names(P4_epi_cellType)<-P4_Oct18_epi@cell.names
#this will be used in P4Oct_mm10_1_2_0_EC2.Rmd

ggplot(data=P4_Oct18_epi@meta.data,aes(genotype,fill=cell_type))+ 
    geom_bar(position="fill")+ theme(axis.text.x = element_text(angle = 45, hjust = 1))

table(P4_Oct18_epi@meta.data$cell_type,P4_Oct18_epi@meta.data$genotype)

                
                 mut  wt
  Basal          143 145
  CiliaSecretory 119  68
  Ciliated       267 203
  Secretory      641 376

compare between genotypes:

DE_P4_ciliated_genotype<-FindMarkers(P4_Oct18_epi,cells.1<-WhichCells(object=P4_Oct18_epi,cells.use = (P4_Oct18_epi@meta.data$genotype=="wt" & P4_Oct18_epi@meta.data$cell_type=="Ciliated" )),cells.2<-WhichCells(object=P4_Oct18_epi,cells.use = (P4_Oct18_epi@meta.data$genotype=="mut" & P4_Oct18_epi@meta.data$cell_type=="Ciliated" )),only.pos = F,logfc.threshold=0,min.pct=0.05)

   |                                                  | 0 % ~calculating  
   |+                                                 | 1 % ~02m 58s      
   |+                                                 | 2 % ~02m 54s      
   |++                                                | 3 % ~02m 51s      
   |++                                                | 4 % ~02m 48s      
   |+++                                               | 5 % ~02m 43s      
   |+++                                               | 6 % ~02m 42s      
   |++++                                              | 7 % ~02m 47s      
   |++++                                              | 8 % ~02m 43s      
   |+++++                                             | 9 % ~02m 39s      
   |+++++                                             | 10% ~02m 37s      
   |++++++                                            | 11% ~02m 35s      
   |++++++                                            | 12% ~02m 32s      
   |+++++++                                           | 13% ~02m 29s      
   |+++++++                                           | 14% ~02m 28s      
   |++++++++                                          | 15% ~02m 25s      
   |++++++++                                          | 16% ~02m 23s      
   |+++++++++                                         | 17% ~02m 21s      
   |+++++++++                                         | 18% ~02m 19s      
   |++++++++++                                        | 19% ~02m 17s      
   |++++++++++                                        | 20% ~02m 15s      
   |+++++++++++                                       | 21% ~02m 13s      
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   |++++++++++++                                      | 23% ~02m 09s      
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   |+++++++++++++                                     | 25% ~02m 06s      
   |+++++++++++++                                     | 26% ~02m 04s      
   |++++++++++++++                                    | 27% ~02m 02s      
   |++++++++++++++                                    | 28% ~02m 00s      
   |+++++++++++++++                                   | 29% ~01m 59s      
   |+++++++++++++++                                   | 30% ~01m 57s      
   |++++++++++++++++                                  | 31% ~01m 55s      
   |++++++++++++++++                                  | 32% ~01m 54s      
   |+++++++++++++++++                                 | 33% ~01m 52s      
   |+++++++++++++++++                                | 34% ~01m 50s      
   |++++++++++++++++++                                | 35% ~01m 48s      
   |++++++++++++++++++                                | 36% ~01m 47s      
   |+++++++++++++++++++                               | 37% ~01m 45s      
   |+++++++++++++++++++                               | 38% ~01m 43s      
   |++++++++++++++++++++                              | 39% ~01m 42s      
   |++++++++++++++++++++                              | 40% ~01m 40s      
   |+++++++++++++++++++++                             | 41% ~01m 38s      
   |+++++++++++++++++++++                             | 42% ~01m 36s      
   |++++++++++++++++++++++                            | 43% ~01m 35s      
   |++++++++++++++++++++++                            | 44% ~01m 33s      
   |+++++++++++++++++++++++                           | 45% ~01m 31s      
   |+++++++++++++++++++++++                           | 46% ~01m 30s      
   |++++++++++++++++++++++++                          | 47% ~01m 28s      
   |++++++++++++++++++++++++                          | 48% ~01m 26s      
   |+++++++++++++++++++++++++                         | 49% ~01m 25s      
   |+++++++++++++++++++++++++                         | 50% ~01m 23s      
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   |+++++++++++++++++++++++++++                       | 53% ~01m 18s      
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   |++++++++++++++++++++++++++++                      | 55% ~01m 15s      
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   |+++++++++++++++++++++++++++++                     | 57% ~01m 11s      
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   |++++++++++++++++++++++++++++++                    | 59% ~01m 08s      
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   |+++++++++++++++++++++++++++++++                   | 61% ~01m 05s      
   |+++++++++++++++++++++++++++++++                   | 62% ~01m 03s      
   |++++++++++++++++++++++++++++++++                  | 63% ~01m 02s      
   |++++++++++++++++++++++++++++++++                  | 64% ~60s          
   |+++++++++++++++++++++++++++++++++                 | 65% ~58s          
   |+++++++++++++++++++++++++++++++++                 | 66% ~57s          
   |++++++++++++++++++++++++++++++++++                | 67% ~55s          
   |++++++++++++++++++++++++++++++++++               | 68% ~53s          
   |+++++++++++++++++++++++++++++++++++               | 69% ~52s          
   |+++++++++++++++++++++++++++++++++++               | 70% ~50s          
   |++++++++++++++++++++++++++++++++++++              | 71% ~48s          
   |++++++++++++++++++++++++++++++++++++              | 72% ~47s          
   |+++++++++++++++++++++++++++++++++++++             | 73% ~45s          
   |+++++++++++++++++++++++++++++++++++++             | 74% ~43s          
   |++++++++++++++++++++++++++++++++++++++            | 75% ~42s          
   |++++++++++++++++++++++++++++++++++++++            | 76% ~40s          
   |+++++++++++++++++++++++++++++++++++++++           | 77% ~38s          
   |+++++++++++++++++++++++++++++++++++++++          | 78% ~37s          
   |++++++++++++++++++++++++++++++++++++++++          | 79% ~35s          
   |++++++++++++++++++++++++++++++++++++++++         | 80% ~33s          
   |+++++++++++++++++++++++++++++++++++++++++         | 81% ~32s          
   |+++++++++++++++++++++++++++++++++++++++++         | 82% ~30s          
   |++++++++++++++++++++++++++++++++++++++++++        | 83% ~28s          
   |++++++++++++++++++++++++++++++++++++++++++        | 84% ~27s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~25s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~23s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~22s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~20s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~18s          
   |+++++++++++++++++++++++++++++++++++++++++++++    | 90% ~17s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~15s          
   |++++++++++++++++++++++++++++++++++++++++++++++   | 92% ~13s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~12s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~10s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~08s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~07s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~05s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~03s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~02s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 02m 46s

DE_P4_ciliated_genotype

write.table(DE_P4_ciliated_genotype,"DE_P4_ciliated_genotype.txt",sep="\t")

P4_ciliated_automatic_geneList<-DE_P4_ciliated_genotype$gene[DE_P4_ciliated_genotype$p_val_adj<0.001 & abs(DE_P4_ciliated_genotype$avg_logFC)>0.5 & abs(DE_P4_ciliated_genotype$pct.1-DE_P4_ciliated_genotype$pct.2)>0.15]

library(ggrepel)

DE_P4_ciliated_genotype$gene<-rownames(DE_P4_ciliated_genotype)
#DE_P4_secretory_genotype$sig<-DE_P4_secretory_genotype$p_val_adj<0.001
DE_P4_ciliated_genotype$threshold<- ifelse(DE_P4_ciliated_genotype$avg_logFC>0 & DE_P4_ciliated_genotype$p_val_adj<0.001, "wt_enrich",ifelse(DE_P4_ciliated_genotype$avg_logFC<0 & DE_P4_ciliated_genotype$p_val_adj<0.001, "mut_enrich","NotSignificant" ) )
ggplot(DE_P4_ciliated_genotype, aes(avg_logFC, -log10(p_val_adj))) + #volcanoplot with avg_logFC versus p_val_adj
    geom_point(aes(col=threshold),size=0.2) + #add points colored by significance
  scale_color_manual(values=c("green", "black","magenta"))+
    ggtitle("P4Ciliated_wt/mut") + geom_text_repel(data=DE_P4_ciliated_genotype[DE_P4_ciliated_genotype$gene %in% P4_ciliated_automatic_geneList,], aes(label=gene), point.padding = 1, box.padding = .3) +
  labs(y = expression(-log[10]*" "*"adjusted pvalue"), x = "avg log fold change") + 
  theme(legend.title = element_blank(), legend.position = "top") 

DE_P4_ciliated_genotype[DE_P4_ciliated_genotype$gene %in% geneList$Primary.ciliary.dyskinesia,]

DE_P4_basal_genotype<-FindMarkers(P4_Oct18_epi,cells.1<-WhichCells(object=P4_Oct18_epi,cells.use = (P4_Oct18_epi@meta.data$genotype=="wt" & P4_Oct18_epi@meta.data$cell_type=="Basal" )),cells.2<-WhichCells(object=P4_Oct18_epi,cells.use = (P4_Oct18_epi@meta.data$genotype=="mut" & P4_Oct18_epi@meta.data$cell_type=="Basal" )),only.pos = F,logfc.threshold=0,min.pct=0.05)

   |                                                  | 0 % ~calculating  
   |+                                                 | 1 % ~02m 11s      
   |+                                                 | 2 % ~02m 03s      
   |++                                                | 3 % ~02m 04s      
   |++                                                | 4 % ~02m 01s      
   |+++                                               | 5 % ~02m 01s      
   |+++                                               | 6 % ~01m 58s      
   |++++                                              | 7 % ~01m 58s      
   |++++                                              | 8 % ~01m 56s      
   |+++++                                             | 9 % ~01m 55s      
   |+++++                                             | 10% ~01m 53s      
   |++++++                                            | 11% ~01m 52s      
   |++++++                                            | 12% ~01m 51s      
   |+++++++                                           | 13% ~01m 50s      
   |+++++++                                           | 14% ~01m 48s      
   |++++++++                                          | 15% ~01m 47s      
   |++++++++                                          | 16% ~01m 45s      
   |+++++++++                                         | 17% ~01m 45s      
   |+++++++++                                         | 18% ~01m 43s      
   |++++++++++                                        | 19% ~01m 42s      
   |++++++++++                                        | 20% ~01m 40s      
   |+++++++++++                                       | 21% ~01m 39s      
   |+++++++++++                                       | 22% ~01m 38s      
   |++++++++++++                                      | 23% ~01m 37s      
   |++++++++++++                                      | 24% ~01m 35s      
   |+++++++++++++                                     | 25% ~01m 34s      
   |+++++++++++++                                     | 26% ~01m 33s      
   |++++++++++++++                                    | 27% ~01m 32s      
   |++++++++++++++                                    | 28% ~01m 30s      
   |+++++++++++++++                                   | 29% ~01m 29s      
   |+++++++++++++++                                   | 30% ~01m 27s      
   |++++++++++++++++                                  | 31% ~01m 26s      
   |++++++++++++++++                                  | 32% ~01m 25s      
   |+++++++++++++++++                                 | 33% ~01m 24s      
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   |+++++++++++++++++++                               | 37% ~01m 19s      
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   |++++++++++++++++++++                              | 39% ~01m 16s      
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   |+++++++++++++++++++++                             | 41% ~01m 14s      
   |+++++++++++++++++++++                             | 42% ~01m 12s      
   |++++++++++++++++++++++                            | 43% ~01m 11s      
   |++++++++++++++++++++++                            | 44% ~01m 10s      
   |+++++++++++++++++++++++                           | 45% ~01m 09s      
   |+++++++++++++++++++++++                           | 46% ~01m 07s      
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   |++++++++++++++++++++++++                          | 48% ~01m 05s      
   |+++++++++++++++++++++++++                         | 49% ~01m 04s      
   |+++++++++++++++++++++++++                         | 50% ~01m 02s      
   |++++++++++++++++++++++++++                        | 51% ~01m 01s      
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   |+++++++++++++++++++++++++++                       | 53% ~59s          
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   |+++++++++++++++++++++++++++++                     | 57% ~54s          
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   |++++++++++++++++++++++++++++++++                  | 63% ~46s          
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   |+++++++++++++++++++++++++++++++++                 | 66% ~43s          
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   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~03s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 02m 05s

DE_P4_basal_genotype

write.table(DE_P4_basal_genotype,"DE_P4_basal_genotype.txt",sep="\t")

P4_bassal_automatic_geneList<-DE_P4_basal_genotype$gene[DE_P4_basal_genotype$p_val_adj<0.001 & abs(DE_P4_basal_genotype$avg_logFC)>0.5 & abs(DE_P4_basal_genotype$pct.1-DE_P4_basal_genotype$pct.2)>0.15]

library(ggrepel)

DE_P4_secretory_genotype<-FindMarkers(P4_Oct18_epi,cells.1<-WhichCells(object=P4_Oct18_epi,cells.use = (P4_Oct18_epi@meta.data$genotype=="wt" & P4_Oct18_epi@meta.data$cell_type=="Secretory" )),cells.2<-WhichCells(object=P4_Oct18_epi,cells.use = (P4_Oct18_epi@meta.data$genotype=="mut" & P4_Oct18_epi@meta.data$cell_type=="Secretory" )),only.pos = F,logfc.threshold=0,min.pct=0.05)

   |                                                  | 0 % ~calculating  
   |+                                                 | 1 % ~05m 56s      
   |+                                                 | 2 % ~05m 59s      
   |++                                                | 3 % ~05m 51s      
   |++                                                | 4 % ~05m 46s      
   |+++                                               | 5 % ~05m 45s      
   |+++                                               | 6 % ~05m 39s      
   |++++                                              | 7 % ~05m 34s      
   |++++                                              | 8 % ~05m 32s      
   |+++++                                             | 9 % ~05m 27s      
   |+++++                                             | 10% ~05m 25s      
   |++++++                                            | 11% ~05m 20s      
   |++++++                                            | 12% ~05m 16s      
   |+++++++                                           | 13% ~05m 13s      
   |+++++++                                           | 14% ~05m 09s      
   |++++++++                                          | 15% ~05m 05s      
   |++++++++                                          | 16% ~05m 01s      
   |+++++++++                                         | 17% ~04m 57s      
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   |++++++++++                                        | 19% ~04m 50s      
   |++++++++++                                        | 20% ~04m 47s      
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   |++++++++++++                                      | 23% ~04m 37s      
   |++++++++++++                                      | 24% ~04m 33s      
   |+++++++++++++                                     | 25% ~04m 29s      
   |+++++++++++++                                     | 26% ~04m 26s      
   |++++++++++++++                                    | 27% ~04m 22s      
   |++++++++++++++                                    | 28% ~04m 18s      
   |+++++++++++++++                                   | 29% ~04m 15s      
   |+++++++++++++++                                   | 30% ~04m 11s      
   |++++++++++++++++                                  | 31% ~04m 08s      
   |++++++++++++++++                                  | 32% ~04m 04s      
   |+++++++++++++++++                                 | 33% ~04m 01s      
   |+++++++++++++++++                                | 34% ~03m 57s      
   |++++++++++++++++++                                | 35% ~03m 54s      
   |++++++++++++++++++                                | 36% ~03m 50s      
   |+++++++++++++++++++                               | 37% ~03m 47s      
   |+++++++++++++++++++                               | 38% ~03m 43s      
   |++++++++++++++++++++                              | 39% ~03m 40s      
   |++++++++++++++++++++                              | 40% ~03m 36s      
   |+++++++++++++++++++++                             | 41% ~03m 32s      
   |+++++++++++++++++++++                             | 42% ~03m 29s      
   |++++++++++++++++++++++                            | 43% ~03m 25s      
   |++++++++++++++++++++++                            | 44% ~03m 22s      
   |+++++++++++++++++++++++                           | 45% ~03m 19s      
   |+++++++++++++++++++++++                           | 46% ~03m 15s      
   |++++++++++++++++++++++++                          | 47% ~03m 12s      
   |++++++++++++++++++++++++                          | 48% ~03m 08s      
   |+++++++++++++++++++++++++                         | 49% ~03m 05s      
   |+++++++++++++++++++++++++                         | 50% ~03m 01s      
   |++++++++++++++++++++++++++                        | 51% ~02m 57s      
   |++++++++++++++++++++++++++                        | 52% ~02m 54s      
   |+++++++++++++++++++++++++++                       | 53% ~02m 50s      
   |+++++++++++++++++++++++++++                       | 54% ~02m 47s      
   |++++++++++++++++++++++++++++                      | 55% ~02m 43s      
   |++++++++++++++++++++++++++++                     | 56% ~02m 39s      
   |+++++++++++++++++++++++++++++                     | 57% ~02m 36s      
   |+++++++++++++++++++++++++++++                     | 58% ~02m 32s      
   |++++++++++++++++++++++++++++++                    | 59% ~02m 29s      
   |++++++++++++++++++++++++++++++                    | 60% ~02m 25s      
   |+++++++++++++++++++++++++++++++                   | 61% ~02m 22s      
   |+++++++++++++++++++++++++++++++                   | 62% ~02m 18s      
   |++++++++++++++++++++++++++++++++                  | 63% ~02m 14s      
   |++++++++++++++++++++++++++++++++                  | 64% ~02m 11s      
   |+++++++++++++++++++++++++++++++++                 | 65% ~02m 07s      
   |+++++++++++++++++++++++++++++++++                 | 66% ~02m 03s      
   |++++++++++++++++++++++++++++++++++                | 67% ~01m 59s      
   |++++++++++++++++++++++++++++++++++               | 68% ~01m 56s      
   |+++++++++++++++++++++++++++++++++++               | 69% ~01m 52s      
   |+++++++++++++++++++++++++++++++++++               | 70% ~01m 48s      
   |++++++++++++++++++++++++++++++++++++              | 71% ~01m 45s      
   |++++++++++++++++++++++++++++++++++++              | 72% ~01m 41s      
   |+++++++++++++++++++++++++++++++++++++             | 73% ~01m 38s      
   |+++++++++++++++++++++++++++++++++++++             | 74% ~01m 34s      
   |++++++++++++++++++++++++++++++++++++++            | 75% ~01m 30s      
   |++++++++++++++++++++++++++++++++++++++            | 76% ~01m 27s      
   |+++++++++++++++++++++++++++++++++++++++           | 77% ~01m 23s      
   |+++++++++++++++++++++++++++++++++++++++          | 78% ~01m 19s      
   |++++++++++++++++++++++++++++++++++++++++          | 79% ~01m 16s      
   |++++++++++++++++++++++++++++++++++++++++         | 80% ~01m 12s      
   |+++++++++++++++++++++++++++++++++++++++++         | 81% ~01m 09s      
   |+++++++++++++++++++++++++++++++++++++++++         | 82% ~01m 05s      
   |++++++++++++++++++++++++++++++++++++++++++        | 83% ~01m 01s      
   |++++++++++++++++++++++++++++++++++++++++++        | 84% ~58s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~54s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~50s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~47s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~43s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~40s          
   |+++++++++++++++++++++++++++++++++++++++++++++    | 90% ~36s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~32s          
   |++++++++++++++++++++++++++++++++++++++++++++++   | 92% ~29s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~25s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~22s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~18s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~14s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~11s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~07s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~04s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 05m 57s

DE_P4_secretory_genotype

write.table(DE_P4_secretory_genotype,"DE_P4_secretory_genotype.txt",sep="\t")

P4_sec_automatic_geneList<-DE_P4_secretory_genotype$gene[DE_P4_secretory_genotype$p_val_adj<0.001 & abs(DE_P4_secretory_genotype$avg_logFC)>0.5 & abs(DE_P4_secretory_genotype$pct.1-DE_P4_secretory_genotype$pct.2)>0.15]

library(ggrepel)

DE_P4_secretory_genotype$gene<-rownames(DE_P4_secretory_genotype)
DE_P4_secretory_genotype$sig<-DE_P4_secretory_genotype$p_val_adj<0.001
volc = ggplot(DE_P4_secretory_genotype, aes(avg_logFC, -log10(p_val_adj))) + #volcanoplot with avg_logFC versus p_val_adj
    geom_point(aes(col=sig),size=0.1) + #add points colored by significance
    scale_color_manual(values=c("black", "red")) + 
    ggtitle("P4secretory_wt/mut") + geom_text_repel(data=DE_P4_secretory_genotype[DE_P4_secretory_genotype$gene %in% P4_sec_automatic_geneList,], aes(label=gene), point.padding = 1, box.padding = .3) +
  labs(y = expression(-log[10]*" "*"adjusted pvalue"), x = "avg log fold change") + 
  theme(legend.title = element_blank(), legend.position = "top") + 
  scale_fill_discrete(labels = c("Not Sig", "adjusted pval < 0.001"))

P4_sec_geneList<-c("Itln1","Retnla","Chil4","Clca1","Cxcl15","Fcgbp","Nfkbia","Hspa5","Ly6k","Ifrd1","Gm42418","Cxcl2","Nfkbiz","Lcn2","Igfbp3","Crip1","Selenbp1","Tppp3","Lyz2","S100a6","Plac8","AU040972","Klk10","Lyz1","Ly6a","Lgals3","Cxcl17","F3","Krt7","Cp","Tsc22d3","Mt1","Chil1","Krt4","Ptprz1","Ifitm1","Txnip","S100a10","Ly6g6c","Hes1","Cldn3","Klk11","Slpi","Baiap2","Plet1","Scnn1a","Lbp","Ltf","Ptges","Muc4","Atp1b1","Atp7b","Ptp4a1","AA467197")

Interleukins:

VlnPlot(object = P4_Oct18_epi, features.plot = c("Il10","Il11","Il12a","Il13","Il15","Il16","Il17a","Il17b","Il17c","Il17d","Il17f","Il18","Il2","Il21","Il22","Il24","Il25","Il27","Il33","Il34","Il4","Il5","Il6","Il7","Il1a","Il1b","Il31"), nCol = 6,x.lab.rot = T,point.size.use = 0.2,group.by="cell_type", legend.position = "left")

All cells have the same value of feature.All cells have the same value of feature.All cells have the same value of feature.All cells have the same value of feature.All cells have the same value of feature.All cells have the same value of feature.All cells have the same value of feature.All cells have the same value of feature.

Interferons: very few are expressed

VlnPlot(object = P4_Oct18_epi, features.plot = grep("Ifn",rownames(P4_Oct18_epi@data),value=T), nCol = 3,x.lab.rot = T,point.size.use = 0.2,group.by="cell_type", legend.position = "left")

All cells have the same value of feature.

antimicrobial effectors: wt vs mut

P4_Oct18_epi@meta.data$type_genotype<-as.factor(paste(P4_Oct18_epi@meta.data$cell_type,P4_Oct18_epi@meta.data$genotype,sep="_"))

P4_Oct18_epi<-SetAllIdent(object = P4_Oct18_epi, id = "type_genotype")
P4_Oct18_epi@ident=factor(P4_Oct18_epi@ident,levels(P4_Oct18_epi@ident)[c(1,2,7,8,5,6,3,4)])
DotPlot(object = P4_Oct18_epi, cols.use = c("forestgreen","magenta3"),genes.plot = rev(c("Nfkbia","Nfkbiz","Retnla","Cxcl17","Cxcl15","Ccl20","Areg","Chil4","Muc5b","Muc4","Pigr","Ltf","Lyz2","Slpi","Lcn2","Sftpd","Sftpb","Defb1","Lgals3","Itln1")),x.lab.rot = T,plot.legend = T,group.by = "ident",do.return=T,col.min = -2,col.max = 2)+rotate()+ theme(axis.text.x = element_text(angle = 45, vjust = 1,hjust=1)) #this scales both genotypes together

df_P4_epi<-FetchData(P4_Oct18_epi,c("Spdef","Creb3l1","Scgb3a2","Scgb1a1","Krt4","Krt13","Foxa3","Aqp3","Aqp4","Aqp5","Gp2","Sostdc1","Smoc2","Krt14","Krt15","Krt5","Rac2","Clic3","res.0.8","genotype","seq_group","specific_type","cell_type","Defb1","Lyz2","Ltf","Sftpa1","Sftpd","Sftpb","Slpi","Lcn2","Pigr","Muc5b","Muc5ac","Chil4","Muc1","Muc2","Muc4","Muc16","Muc20","Lbp","Cd14","Tlr4","Tlr2","Myd88","Ticam1","Itln1","Lgals3","Reg3g","Nod1","Nod2","Ddx58","Ifih1","Dhx58","Ccl5","Cxcl10","Cxcl2","Cxcl1","Pf4","Cxcl12","Cxcl14","Cxcl15","Cxcl16","Cxcl17","Ccl2","Ccl7","Ccl17","Ccl20","Ccl21a","Ccl25","Ccl27a","Ccl28","Cx3cl1","Il10","Tnf","S100a8","S100a9","Il6","Il18","Il1b","Il1rl1","Ccl11","Ccl24","Il33","Il25","Tslp","F2rl1","Retnla","Alox15","Alox5","Gata2","Tgfb2","Tgfb1","Ormdl3","Ptges","Ptgds","Ptgs2","Hpgds","Tbxas1","Areg","Il2","Il34","Il15","Ifnlr1","Nfkbiz","Nfkbia"))

MicrobialSensing:

for (i in c("Lbp","Cd14","Tlr4","Tlr2","Myd88","Ticam1","Itln1","Reg3g","Lgals3","Nod1","Nod2","Ddx58","Ifih1","Dhx58"))
{
pdf(file = paste("Manuscript/MicrobialSensing_genotype/P4/",i,".pdf", sep = ""), width = 6, height = 5)
print(ggplot(df_P4_epi,aes_string(x="genotype",y=i))+facet_grid(.~cell_type)+geom_dotplot(binaxis="y",aes(fill=genotype),binwidth=0.05,stackdir="center",position=position_dodge(0.8), dotsize=0.2)+stat_compare_means(comparisons = list(c("wt", "mut")),method="wilcox.test",size=4,label="p.adj")+ stat_summary(aes(color=genotype),fun.data=mean_sdl, fun.args = list(mult=1), 
                 geom="pointrange",position=position_dodge(0.7))+ theme(axis.text.x = element_text(angle = 45,hjust=1),strip.text.x = element_text(size = 9, colour = "black", angle = 0)))
dev.off()
}

antimicrobial effectors:

for (i in c("Muc1","Muc4","Muc16","Muc20","Muc5b","Muc5ac","Muc2","Defb1","Lyz2","Ltf","Sftpa1","Sftpd","Sftpb","Slpi","Lcn2","Pigr","Chil4"))
{
pdf(file = paste("Manuscript/Effectors_genotype/P4/",i,".pdf", sep = ""), width = 6, height = 5)
print(ggplot(df_P4_epi,aes_string(x="genotype",y=i))+facet_grid(.~cell_type)+geom_dotplot(binaxis="y",aes(fill=genotype),binwidth=0.05,stackdir="center",position=position_dodge(0.8), dotsize=0.2)+stat_compare_means(comparisons = list(c("wt", "mut")),method="wilcox.test",size=4,label="p.adj")+ stat_summary(aes(color=genotype),fun.data=mean_sdl, fun.args = list(mult=1), 
                 geom="pointrange",position=position_dodge(0.7))+ theme(axis.text.x = element_text(angle = 45,hjust=1),strip.text.x = element_text(size = 9, colour = "black", angle = 0)))
dev.off()
}

chemokines:

for (i in c("Ccl5","Cxcl10","Cxcl2","Cxcl1","Pf4","Cxcl12","Cxcl14","Cxcl15","Cxcl16","Cxcl17","Ccl2","Ccl7","Ccl17","Ccl20","Ccl21a","Ccl25","Ccl27a","Ccl28","Cx3cl1"))
{
pdf(file = paste("Manuscript/chemokines_genotype/P4/",i,".pdf", sep = ""), width = 6, height = 5)
print(ggplot(df_P4_epi,aes_string(x="genotype",y=i))+facet_grid(.~cell_type)+geom_dotplot(binaxis="y",aes(fill=genotype),binwidth=0.05,stackdir="center",position=position_dodge(0.8), dotsize=0.2)+stat_compare_means(comparisons = list(c("wt", "mut")),method="wilcox.test",size=4,label="p.adj")+ stat_summary(aes(color=genotype),fun.data=mean_sdl, fun.args = list(mult=1), 
                 geom="pointrange",position=position_dodge(0.7))+ theme(axis.text.x = element_text(angle = 45,hjust=1),strip.text.x = element_text(size = 9, colour = "black", angle = 0)))
dev.off()
}

Th2:

for (i in c("Il10","Tnf","S100a8","S100a9","Il6","Il18","Il1b","Il1rl1","Ccl11","Ccl24","Il33","Il25","Tslp","F2rl1","Retnla","Alox15","Alox5","Gata2","Tgfb2","Tgfb1","Ormdl3","Ptges","Ptgds","Ptgs2","Hpgds","Tbxas1","Areg"))
{
pdf(file = paste("Manuscript/Th2_genotype/P4/",i,".pdf", sep = ""), width = 6, height = 5)
print(ggplot(df_P4_epi,aes_string(x="genotype",y=i))+facet_grid(.~cell_type)+geom_dotplot(binaxis="y",aes(fill=genotype),binwidth=0.05,stackdir="center",position=position_dodge(0.8), dotsize=0.2)+stat_compare_means(comparisons = list(c("wt", "mut")),method="wilcox.test",size=4,label="p.adj")+ stat_summary(aes(color=genotype),fun.data=mean_sdl, fun.args = list(mult=1), 
                 geom="pointrange",position=position_dodge(0.7))+ theme(axis.text.x = element_text(angle = 45,hjust=1),strip.text.x = element_text(size = 9, colour = "black", angle = 0)))
dev.off()
}

res.used <- 1.2

P4_Oct18_epi <- FindClusters(object = P4_Oct18_epi, reduction.type = "pca", dims.use = 1:n.pcs.sub, 
                     resolution = res.used, print.output = 0, force.recalc = T)

P4_Oct18_epi <- RunTSNE(object = P4_Oct18_epi, dims.use = 1:n.pcs.sub, perplexity=30)

TSNEPlot(object = P4_Oct18_epi, do.label = T,pt.size = 0.4,group.by="res.1.2")

table(P4_Oct18_epi@meta.data$res.0.8,P4_Oct18_epi@meta.data$res.1.2)

   
      0   1  10  11   2   3   4   5   6   7   8   9
  0 311   0   0   0   0   0   0   1   0   3   0   0
  1   0   0   0   0   0 192   0   1   0   0   0 109
  2   0   0   0  33 205   0   0   0   0   0   0   0
  3   0 226   1   0   0   1   0   0   1   0   0   0
  4   0   0   2   0   0   0 185   0   0   0   0   0
  5   0   0   0   0   0   0   0 169   0   0   0   2
  6   0   0   0   0   0   0   0   0 152   0   0   0
  7   0   0   0   0   0   0   0   0   0 136   0   0
  8   0   0   0   0   0   0   0   0   0   0 135   0
  9   0   0  97   0   0   0   0   0   0   0   0   0

res.used <- 1.4

P4_Oct18_epi <- FindClusters(object = P4_Oct18_epi, reduction.type = "pca", dims.use = 1:n.pcs.sub, 
                     resolution = res.used, print.output = 0, force.recalc = T)

P4_Oct18_epi <- RunTSNE(object = P4_Oct18_epi, dims.use = 1:n.pcs.sub, perplexity=30)

TSNEPlot(object = P4_Oct18_epi, do.label = T,pt.size = 0.4,group.by="res.1.4")

table(P4_Oct18_epi@meta.data$res.0.8,P4_Oct18_epi@meta.data$res.1.4)

   
      0   1  10  11  12  13   2   3   4   5   6   7   8   9
  0   0   0   0   0   0   0   0   0 172   2 141   0   0   0
  1   0   0   0   0   0   0 191   0   0   1   0   0   0 110
  2   0 205   0   0   0  33   0   0   0   0   0   0   0   0
  3 224   0   1   0   1   0   1   0   1   0   1   0   0   0
  4   0   0   2   0   0   0   0 185   0   0   0   0   0   0
  5   0   0   0   0   0   0   0   0   0 169   0   0   0   2
  6   0   0   0  89  63   0   0   0   0   0   0   0   0   0
  7   0   0   0   0   0   0   0   0   0   0   0 136   0   0
  8   0   0   0   0   0   0   0   0   0   0   0   0 135   0
  9   0   0  97   0   0   0   0   0   0   0   0   0   0   0

table(P4_Oct18_epi@meta.data$genotype,P4_Oct18_epi@meta.data$res.1.4)

     
        0   1  10  11  12  13   2   3   4   5   6   7   8   9
  mut 223 135  46  78  64  24 191 117   5 107   3   2  65 110
  wt    1  70  54  11   0   9   1  68 168  65 139 134  70   2

P4_Oct18_epi@meta.data$specific_type<-mapvalues(P4_Oct18_epi@meta.data$res.0.8,from=c("0","1","2","3","4","5","6","7","8","9"),to=c("Secretory","Secretory-Krt4","Ciliated","Secretory-Krt4","CiliaSecretory","Secretory-Krt4","Basal","Basal","Ciliated","Ciliated-Foxn4"))

P4_Oct18_epi@meta.data$specific_type_1.4<-mapvalues(P4_Oct18_epi@meta.data$res.1.4,from=c("0","1","2","3","4","5","6","7","8","9","10","11","12","13"),to=c("Secretory","Ciliated","Secretory","CiliaSecretory","Secretory","CyclingSecretory","Secretory","Basal-Sostdc1","Ciliated","Secretory-Krt4","Ciliated","CyclingBasal","Basal","Ciliated"))

to support this identification:

table(P4_Oct18_epi@meta.data$specific_type_1.4,P4_Oct18_epi@meta.data$genotype)

                  
                   mut  wt
  Basal             64   0
  Basal-Sostdc1      2 134
  CiliaSecretory   117  68
  Ciliated         270 203
  CyclingBasal      78  11
  CyclingSecretory 107  65
  Secretory        422 309
  Secretory-Krt4   110   2

save(P4_Oct18_epi,file="P4_epi_mm10.1.2.0.RData")